Search NASA⌕ Search

SEARCH · Search NASA

Results for “Diffusion model”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integrability breaking in the Rule 54 cellular automaton

Cellular automata have recently attracted a lot of attention as testbeds to explore the emergence of many-body quantum chaos and hydrodynamics. We consider the Rule 54 model, one of the simplest interacting integrable models featuring two species of quasiparticles (solitons), in the presence of an integrability-breaking perturbation that allows solitons to backscatter. Here, we study the onset of thermalization and diffusive hydrodynamics in this model, compute perturbatively the diffusion constant of tracer particles, and comment on its relation to transport coefficients.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1985

Large-cell Monte Carlo renormalization of irreversible growth processes

Monte Carlo sampling is applied to a recently formulated direct-cell renormalization method for irreversible, disorderly growth processes. Large-cell Monte Carlo renormalization is carried out for various nonequilibrium problems based on the formulation dealing with relative probabilities. Specifically, the method is demonstrated by application to the 'true' self-avoiding walk and the Eden model of growing animals for d = 2, 3, and 4 and to the invasion percolation problem for d = 2 and 3. The results are asymptotically in agreement with expectations; however, unexpected complications arise, suggesting the possibility of crossovers, and in any case, demonstrating the danger of using small cells alone, because of the very slow convergence as the cell size b is extrapolated to infinity. The difficulty of applying the present method to the diffusion-limited-aggregation model, is commented on.

Nakanishi, H.↗
DOE OSTI2023

Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids

Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. Here in this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Optical Fier Based System for Multiple Thermophysical Properties for Glove Box, Hot Cell and In-Pile Application

Thermal diffusivity of materials is of interest in nuclear applications at temperatures in excess of 2000°C. Commercial laser flash apparatus (LFA) that heats samples with a furnace typically do not reach these elevated temperatures nor are they easily adapted to a glove-box or hot cell environment. In this research, we performed work on an experimental technique using single laser surface heating, i.e. heating the disk sample only at its front surface with the continuous wave (CW) laser, to allow measurement of thermal diffusivity at very high temperatures within a small chamber. Thermal diffusivity is measured using a separate pulsed laser on the front side and IR detector on the rear side. The new way of heating provides easy operation in comparison to other heating methods. The measurement of sample reference temperature is needed for the measured thermal diffusivity. A theoretical model was developed to describe transient heat transfer across the sample due to the laser pulse, starting from the steady state temperature of the sample heated by the CW laser. The experimental setup was established with a 500W CW laser and maximum 50 Joule pulse laser irradiated at the front surface of the sample. The induced temperature rise at the rear surface, along with the steady-state temperature at the front surface, was recorded for the determination of thermal diffusivity and the sample temperature. Three samples were tested in vacuum over a wide temperature range of 500°C to 2100°C, including graphite, Inconel 600 and tungsten. The latter two samples were coated with sprayed graphite on their front surfaces in order to achieve surface absorption/emission needs, i.e. high absorptivity of the front surface against relatively low emissivity of the rear surface. Thermal diffusivity of graphite determined by our system are within a 5% difference of the commercial LFA data at temperatures below 1300°C and agree well with its trend at higher temperatures. Good agreement would also exist for Inconel 600 and tungsten. Despite large uncertainty of measuringthe sample temperature, the uncertainties of thermal diffusivity are less than 6% for all samples at elevated temperatures. The results indicate that single laser surface heating could be convenient and practical for the application of the LFA measurements without extra uncertainty, as temperature dependence of thermal diffusivity is usually negligible in the sample. Moreover, it is concluded that unequal surface treatment, i.e., high absorption on the front side and low emission on the rear side, greatly improves the measurement in serval aspects: less power requirement of the CW laser, less uncertainty of measured thermal diffusivity, and more uniform temperature distribution in the sample. The result of this research can be used as a general guideline for the design of this type of measurement system for nuclear applications. It can also be used directly to design and build a system similar to the one implemented in this project.

36 MATERIALS SCIENCE↗
DOE OSTI2017

On the Radio-emitting Particles of the Crab Nebula: Stochastic Acceleration Model

The broadband emission of pulsar wind nebulae (PWNe) is well described by non-thermal emissions from accelerated electrons and positrons. However, the standard shock acceleration model of PWNe does not account for the hard spectrum in radio wavelengths. The origin of the radio-emitting particles is also important to determine the pair production efficiency in the pulsar magnetosphere. Here, we propose a possible resolution for the particle energy distribution in PWNe; the radio-emitting particles are not accelerated at the pulsar wind termination shock but are stochastically accelerated by turbulence inside PWNe. We upgrade our past one-zone spectral evolution model to include the energy diffusion, i.e., the stochastic acceleration, and apply the model to the Crab Nebula. A fairly simple form of the energy diffusion coefficient is assumed for this demonstrative study. For a particle injection to the stochastic acceleration process, we consider the continuous injection from the supernova ejecta or the impulsive injection associated with supernova explosion. The observed broadband spectrum and the decay of the radio flux are reproduced by tuning the amount of the particle injected to the stochastic acceleration process. The acceleration timescale and the duration of the acceleration are required to be a few decades and a few hundred years, respectively. Our results imply that some unveiled mechanisms, such as back reaction to the turbulence, are required to make the energies of stochastically and shock-accelerated particles comparable.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

GEMS X-ray Polarimeter Performance Simulations

The Gravity and Extreme Magnetism Small explorer (GEMS) is an X-ray polarization telescope selected as a NASA small explorer satellite mission. The X-ray Polarimeter on GEMS uses a Time Projection Chamber gas proportional counter to measure the polarization of astrophysical X-rays in the 2-10 keV band by sensing the direction of the track of the primary photoelectron excited by the incident X-ray. We have simulated the expected sensitivity of the polarimeter to polarized X-rays. We use the simulation package Penelope to model the physics of the interaction of the initial photoelectron with the detector gas and to determine the distribution of charge deposited in the detector volume. We then model the charge diffusion in the detector,and produce simulated track images. Within the track reconstruction algorithm we apply cuts on the track shape and focus on the initial photoelectron direction in order to maximize the overall sensitivity of the instrument, using this technique we have predicted instrument modulation factors nu(sub 100) for 100% polarized X-rays ranging from 10% to over 60% across the 2-10 keV X-ray band. We also discuss the simulation program used to develop and model some of the algorithms used for triggering, and energy measurement of events in the polarimeter.

Baumgartner, Wayne H.↗
DOE OSTI2018

Drift turbulence, particle transport, and anomalous dissipation at the reconnecting magnetopause

Here, using fully kinetic 3D simulations, the reconnection dynamics of asymmetric current sheets are examined at the Earth's magnetopause. The plasma parameters are selected to model MMS magnetopause diffusion region crossings with guide fields of 0.1, 0.4, and 1 of the reconnecting magnetosheath field. In each case, strong drift-wave fluctuations are observed in the lower-hybrid frequency range at the steep density gradient across the magnetospheric separatrix. These fluctuations give rise to cross-field electron particle transport. In addition, this turbulent mixing leads to significantly enhanced electron parallel heating in comparison to 2D simulations. We study three different methods of quantifying the anomalous dissipation produced by the drift fluctuations, based on spatial averaging, temporal averaging, and temporal averaging followed by integrating along magnetic field lines. A comparison of different methods reveals complications in identifying and measuring the anomalous dissipation. Nevertheless, the anomalous dissipation from short wavelength drift fluctuations appears weak for each case, and the reconnection rates observed in 3D are nearly the same as in 2D models. Finally, the 3D simulations feature a number of interesting new features that are consistent with recent MMS observations, including cold beams of magnetosheath electrons that penetrate into the hotter magnetospheric inflow, the related observation of decreasing temperature in regions of increasing total density, and an effective turbulent diffusion coefficient that agrees with predictions from quasi-linear theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Comparison of Directionally Solidified Samples Solidified Terrestrially and Aboard the International Space Station

This article reports research that has been carried out under the aegis of NASA as part of a collaboration between ESA and NASA for solidification experiments on the International Space Station (ISS). The focus has been on the effect of convection on the microstructural evolution and macrosegregation in hypoeutectic Al-Si alloys during directional solidification (DS). Terrestrial DS-experiments have been carried out at Cleveland State University (CSU) and under microgravity on the International Space Station (ISS). The thermal processing-history of the experiments is well defined for both the terrestrially processed samples and the ISS-processed samples. As of this writing, two dendritic metrics was measured: primary dendrite arm spacings and primary dendrite trunk diameters. We have observed that these dendrite-metrics of two samples grown in the microgravity environment show good agreements with models based on diffusion controlled growth and diffusion controlled ripening, respectively. The gravity-driven convection (i.e., thermosolutal convection) in terrestrially grown samples has the effect of decreasing the primary dendrite arm spacings and causes macrosegregation. Dendrite trunk diameters also show differences between the earth- and space-grown samples. In order to process DS-samples aboard the ISS, the dendritic seed crystals were partially remelted in a stationary thermal gradient before the DS was carried out. Microstructural changes and macrosegregation effects during this period are described and have modeled.

Angart, S.↗
NASA NTRS1998

Numerical Modeling of Physical Vapor Transport in Contactless Crystal Growth Geometry

Growth from the vapor under conditions of limited contact with the walls of the growth ampoule is beneficial for the quality of the growing crystal due to reduced stress and contamination which may be caused by interactions with the growth container. The technique may be of a particular interest for studies on crystal growth under microgravity conditions: elimination of some factors affecting the crystal quality may make interpretation of space-conducted processes more conclusive and meaningful. For that reason, and as a part of our continuing studies on 'contactless' growth technique, we have developed a computational model of crystal growth process in such system. The theoretical model was built, and simulations were performed using the commercial computational fluid dynamics code, (CFD) ACE. The code uses an implicit finite volume formulation with a gray discrete ordinate method radiation model which accounts for the diffuse absorption and reflection of radiation throughout the furnace. The three-dimensional model computes the heat transfer through the crystal, quartz, and gas both inside and outside the ampoule, and mass transport from the source to the crystal and the sink. The heat transport mechanisms by conduction, natural convection, and radiation, and mass transport by diffusion and convection are modeled simultaneously and include the heat of the phase transition at the solid-vapor interfaces. As the thermal boundary condition, temperature profile along the walls of the furnace is used. For different thermal profiles and furnace and ampoule dimensions, the crystal growth rate and development of the crystal-vapor and source-vapor interfaces (change of the interface shape and location with time) are obtained. Super/under-saturation in the ampoule is determined and critical factors determining the 'contactless' growth conditions are identified and discussed. The relative importance of the ampoule dimensions and geometry, the furnace dimensions and its temperature, and the properties of the grown material are analyzed. The results of the simulations are compared with related experimental results on growth of CdTe, CdZnTe, ZnTe, PbTe, and PbSnTe crystals by this technique.

Palosz, W.↗
NASA NTRS1981

Spectral albedos of midlatitude snowpacks

Spectral albedos of impure-nonhomogeneous snowpacks, typical of midlatitudes, from 400 to 2200 nm were modeled through a numerical solution of the radiative transfer equation in the two-stream approximation. Discrete depth-dependent values of density, grain size and impurity concentration were used to characterize the snowpacks. The model is for diffuse incident radiation, and the numerical method is based on doubling and invariant imbedding. The effect of soot impurities on snowpack albedos is illustrated when a snowpack is several centimeters deep and soot reduces the albedos at visible wavelengths, however, when a snowpack is only a few centimeters deep, soot may increase the albedos at visible wavelengths. By adjusting soot content and snow grain size, good quantitative agreement with some observations at the Cascade Mountains (Washington) and at Point Barrow (Alaska) are obtained; however, the model grain sizes are found to be fifty to four hundred percent larger than the measured values. For satellite snowcover observations, a model for effective albedo of partially snow-covered areas was developed and compared with some NOAA-2 observations of the southeastern United States.

Choudhury, B.↗
DOE OSTI2022

Estimates of Quantum Tunneling Effects for Hydrogen Diffusion in PuO 2

We detail the estimation of activation energies and quantum nuclear vibrational tunneling effects for hydrogen diffusion in PuO 2 based on Density Functional Theory calculations and a quantum double well approximation. We find that results are relatively insensitive to choice of exchange correlation functional. In addition, the representation of spin in the system and use of an extended Hubbard U correction has only a small effect on hydrogen point defect formation energies when the PuO 2 lattice is held fixed at the experimental density. We then compute approximate activation energies for transitions between hydrogen interstitial sites seeded by a semi-empirical quantum model and determine the quantum tunneling enhancement relative to classical kinetic rates. Our model indicates that diffusion rates in H/PuO 2 systems could be enhanced by more than one order of magnitude at ambient conditions and that these effects persist at high temperature. The method we propose here can be used as a fast screening tool for assessing possible quantum nuclear vibrational effects in any number of condensed phase materials and surfaces, where hydrogen hopping tends to follow well defined minimum energy pathways.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 1. Flow without reactions

Here we present a fully analytical, heuristic model – the “Analytical Transport Network Model” – for steady-state, diffusive, potential flow through a 3-D network. Employing a combination of graph theory, linear algebra, and geometry, the model explicitly relates a microstructural network's topology and the morphology of its channels to an effective material transport coefficient (a general term meant to encompass, e.g., conductivity or diffusion coefficient). The model's transport coefficient predictions agree well with those from electrochemical fin (ECF) theory and finite element analysis (FEA), but are computed 0.5–1.5 and 5–6 orders of magnitude faster, respectively. In addition, the theory explicitly relates a number of morphological and topological parameters directly to the transport coefficient, whereby the distributions that characterize the structure are readily available for further analysis. Furthermore, ATN's explicit development provides insight into the nature of the tortuosity factor and offers the potential to apply theory from network science and to consider the optimization of a network's effective resistance in a mathematically rigorous manner. The ATN model's speed and relative ease-of-use offer the potential to aid in accelerating the design (with respect to transport), and thus reducing the cost, of energy materials.

25 ENERGY STORAGE↗
DOE OSTI2021

Scattering by whistler-mode waves during a quiet period perturbed by substorm activity

We study the dynamics of radiation belt electrons during a 10-day quiet period perturbed by substorm activity and preceding a high-speed stream (HSS), aiming at a global description of the radiation belts in L-shell, L in [2, 6], and energy [0.1, 10] MeV. We combine Van Allen Probes observations and Fokker-Planck numerical simulations of pitch-angle diffusion. The Fokker-Planck model uses event-driven pitch angle diffusion coefficients from whistler-mode waves, built from the wave properties and the ambient plasma density measurements from the Van Allen Probes. We first find this event has some similar characteristics to regular quiet times previously studied; a widely extended plasmasphere within which we observe strong and varying whistler-mode waves. These ambient conditions lead to strong pitch-angle scattering, which contributes to the creation of a wide slot region as well as a significant decay of the outer radiation belts, which are observed and qualitatively well simulated. In addition, we find the substorm activity causes short duration (within ± 4h) decay of the plasma density and a lowering amplitude of the whistler-mode waves within the plasmasphere, both causing opposite effects in terms of pitch angle diffusion. This leads to a diminution of pitch-angle diffusion at the time of the main substorm activity. Conversely, whistler-mode waves become enhanced in the time periods between the substorm injections. All effects cumulated, we find an enhancement of pitch angle diffusion by whistler-mode waves above L~4.7 during the 10-day period. This directly relates to the combination of quietness and substorm activity which allows pitch angle diffusing of up to 1 MeV electrons in the outer belt. Relativistic electrons of 1–2 MeV remain trapped in the outer belt, from L~4.7 to L~5.2, forming, in both the observations and the simulations, a distinct pocket of remnant electrons.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1982

An iterative radiative transfer code for ocean-atmosphere systems

The details of an iterative radiative transfer code for computing the intensity and degree of polarization of diffuse radiation in models of the ocean-atmosphere system are described. The present code neglects the upwelling radiation from below the ocean surface and as such can be applied to the part of the spectrum where the absorption by water is strong. To establish the reliability of the numerical scheme and the computer code, the results are compared with those of Fraser and Walker (1968), Dave (1972), and Mullamaa (1964); they are found to be in excellent agreement. The computations also show that both the intensity and the degree of polarization of the upwelling diffuse radiation at the top of the atmosphere vary significantly when the rough ocean at the base of the atmosphere is replaced by a Lambertian surface that reflects the same energy as the rough ocean.

Ahmad, Z.↗
NASA NTRS1977

Gravitational effects on chemically reacting laminar boundary layer flows over a horizontal flat plate

A theoretical study of the chemically reacting laminar boundary layer flow over a horizontal flat plate with gravitationally induced buoyant force is presented. A diffusion flame sheet model was used to describe the combustion process. The effects of gravity on the purely force convection flow can be characterized by a dimensionless coordinate quantity, which is involved in the generation of the governing equations. A numerical solution of the zero and first order governing equations subject to the appropriate physical boundary conditions was obtained. It is shown that the cross stream buoyancy induced body force acts effectively to produce a streamwise pressure gradient in the fluid adjacent to the plate surface. It is concluded that buoyancy plays an important role in boundary layer diffusion flames.

Lavid, M.↗
DOE OSTI2017

Bioethanol enrichment using zeolite membranes: Molecular modeling, conceptual process design and techno-economic analysis

Lignocellulosic biomass-derived ethanol offers a potential alternative to fossil-derived fuels. However, the energy-intensive nature of its recovery process, i.e., a sequence of two distillation columns – beer and rectification columns - enriching from 5 to 37, and 37–93 wt%, respectively, limits its environmental and economic benefits. Here, in this paper, we assess the potential of using zeolite membranes for bioethanol recovery. Atomistic-level and molecular dynamic simulations are performed to determine adsorption and diffusion properties of the ethanol-water mixture in zeolite membranes. These properties are modeled using the real adsorption solution theory and the Maxwell-Stefan equations to describe permeation through the zeolite membranes. A comparison of steady state permeance and selectivity predicted by the model with the values from experiments suggests that the simulated membranes are more permeable and selective than the real membranes. This is attributed to the presence of structural non-idealities and hydrophilic defects in real membranes, while the adsorption and diffusion properties obtained using molecular simulations reflect the behavior in ideal crystals. Thus, a reduced diffusivity model with an empirical relation for enhanced water adsorption is used to capture similar performance as that obtained by real membranes. This model is further used in developing conceptual process designs to assess the viability of zeolite membranes for bioethanol enrichment in industry. Both hydrophobic and hydrophilic zeolite membranes are considered. Hydrophobic zeolite membranes show potential for energy savings but lack in separation performance. On the other hand, hydrophilic zeolite membranes can achieve the separation target but result in no energy savings. Thus, a configuration that uses a combination of hydrophobic and hydrophilic membranes is proposed. It can achieve the separation target and results in 15% energy savings over distillation. Techno-economic analysis suggests that ~ 10-fold improvements in permeation or equivalent cost reductions are required for economic viability of this scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗